Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T...
Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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More information
Scope and Contents
Contents
Purpose
Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of...
Alternative Titles
Full title
Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI
Authors, Artists and Contributors
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Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7666676
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7666676
Other Identifiers
ISSN
0028-3940
E-ISSN
1432-1920
DOI
10.1007/s00234-020-02502-z
